{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "# 数据集成\n",
    "\n",
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "'''\n",
    "with open('datas/basicInfo_train.csv','rb') as f:\n",
    "    reader = csv.reader(f)\n",
    "    dataset = []\n",
    "    for row in reader:\n",
    "        dataset.append(row)\n",
    "    df = pd.DataFrame(dataset)\n",
    "'''\n",
    "basicInfo = pd.DataFrame.from_csv('datas/basicInfo_train.csv', header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)\n",
    "historyInfo = pd.DataFrame.from_csv('datas/history_train.csv', header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)\n",
    "defaultInfo = pd.DataFrame.from_csv('datas/default_train.csv', header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=False, infer_datetime_format=False)\n",
    "combineInfo = pd.concat([basicInfo,historyInfo,defaultInfo],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRED_LIMIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>654.4</td>\n",
       "      <td>691.0</td>\n",
       "      <td>652.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5662.8</td>\n",
       "      <td>5791.8</td>\n",
       "      <td>5909.4</td>\n",
       "      <td>400.0</td>\n",
       "      <td>403.8</td>\n",
       "      <td>240.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>213.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>3829.2</td>\n",
       "      <td>3826.2</td>\n",
       "      <td>400.0</td>\n",
       "      <td>7336.2</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>137.8</td>\n",
       "      <td>135.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3878.8</td>\n",
       "      <td>3923.8</td>\n",
       "      <td>4004.8</td>\n",
       "      <td>500.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>131.4</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.2</td>\n",
       "      <td>-31.8</td>\n",
       "      <td>113.4</td>\n",
       "      <td>76.0</td>\n",
       "      <td>120.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.2</td>\n",
       "      <td>337.4</td>\n",
       "      <td>308.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2442.2</td>\n",
       "      <td>2358.6</td>\n",
       "      <td>743.8</td>\n",
       "      <td>665.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.4</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2601.4</td>\n",
       "      <td>2782.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2601.4</td>\n",
       "      <td>224.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1703.4</td>\n",
       "      <td>4457.4</td>\n",
       "      <td>2733.6</td>\n",
       "      <td>4363.6</td>\n",
       "      <td>1993.2</td>\n",
       "      <td>1716.6</td>\n",
       "      <td>4460.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>126000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CRED_LIMIT  SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  PAY_3  PAY_4  \\\n",
       "ID                                                                          \n",
       "1         4000    1          2         1   24      2      2     -1     -1   \n",
       "2        24000    1          2         2   26     -1      2      0      0   \n",
       "3        10000    1          2         1   37      0      0      0      0   \n",
       "4        10000    0          2         1   57     -1      0     -1      0   \n",
       "5        10000    0          1         2   37      0      0      0      0   \n",
       "6        20000    1          2         2   23      0     -1     -1      0   \n",
       "7        28000    1          3         1   28      0      0      2      0   \n",
       "8         4000    0          3         2   35     -2     -2     -2     -2   \n",
       "9        52000    1          1         2   51     -1     -1     -1     -1   \n",
       "10      126000    1          2         2   41     -1      0     -1     -1   \n",
       "\n",
       "    PAY_5   ...     BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "ID          ...                                                           \n",
       "1      -2   ...           0.0        0.0        0.0       0.0     137.8   \n",
       "2       0   ...         654.4      691.0      652.2       0.0     200.0   \n",
       "3       0   ...        5662.8     5791.8     5909.4     400.0     403.8   \n",
       "4       0   ...        4188.0     3829.2     3826.2     400.0    7336.2   \n",
       "5       0   ...        3878.8     3923.8     4004.8     500.0     363.0   \n",
       "6       0   ...          44.2      -31.8      113.4      76.0     120.2   \n",
       "7       0   ...        2442.2     2358.6      743.8     665.8       0.0   \n",
       "8      -1   ...           0.0     2601.4     2782.4       0.0       0.0   \n",
       "9      -1   ...        1703.4     4457.4     2733.6    4363.6    1993.2   \n",
       "10     -1   ...        1300.0     1300.0      574.0     200.0    1300.0   \n",
       "\n",
       "    PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  Default  \n",
       "ID                                                   \n",
       "1        0.0       0.0       0.0       0.0        1  \n",
       "2      200.0     200.0       0.0     400.0        1  \n",
       "3      240.0     220.0     213.8     200.0        0  \n",
       "4     2000.0    1800.0     137.8     135.8        0  \n",
       "5      131.4     200.0     200.0     160.0        0  \n",
       "6        0.0     116.2     337.4     308.4        0  \n",
       "7       86.4     200.0     200.0     200.0        0  \n",
       "8        0.0    2601.4     224.4       0.0        0  \n",
       "9     1716.6    4460.2       0.0     728.0        0  \n",
       "10    1300.0    1300.0     574.0       0.0        0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看前10条数据\n",
    "combineInfo[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8e8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#性别分析\n",
    "gender = combineInfo.groupby('SEX')['Default'].mean().reset_index()\n",
    "plt.xticks((0,1),(u\"Male\",u\"Female\"))\n",
    "plt.xlabel(u\"Gender\")\n",
    "plt.ylabel(u\"Counts\")\n",
    "plt.bar(gender.SEX,gender.Default,0.1,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IEIunpFpdilJDEhTUv6b/vSMN1LUE9ljGPofh4TUlxEWG8ugy/xilOFga/Jdx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\noK9ttgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3fea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#教育程度与default值的相关性分析\n",
    "edu = combineInfo.groupby('EDUCATION')['Default'].mean()\n",
    "plt.plot(edu)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y/wdO8JNJ+8USvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed35630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#婚姻状况分析\n",
    "marriage = combineInfo.groupby('MARRIAGE')['Default'].mean().reset_index()\n",
    "plt.bar(marriage.MARRIAGE,marriage.Default,0.5,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
